{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()\n",
    "#存在一些缺失值，记为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NNLMCMysoKSmJctfN27t7j1J08ASXDm7eXTs1XT8mh/+8cigvrtvL1558S8Ev0spEE/pFQK+Ix9nA7ij3H9W27j7b3fPdPT8rKyvKXTdviwqLAbh4UMv7eWaMzeX7nxjC39e8x1ce01W7Iq1JNKG/Asgzsz5mlgxMB+ZFuf8XgMvMLD18Avey8LpWb1FhCYO6daB7p+ZxFW593XRRH3501UdYsKGYz81ZTunJU/EuSURioM7Qd/cK4Daqw3o98LS7rzWzu83sSgAzO9/MioBPAQ+Z2drwtgeAH1H9i2MFcHd4Xat2tKyCgu0HmDiw5R3lR7rhgt48MH0kb24/yKf/5w12HToR75JE5BxFdYbR3ecD82us+0HE8gqqu25q23YOMOccamxxFm/ax6lKZ9KALvEu5ZxdObwH6SlJ3PLYm0z71WJ+NyOfEb3iP3GciDSMrshtBIsKS0htk0h+buu4JGF8XhZ/vmUs7ZJDfPqhN3hi2Q7cdZN1kZaoZYwlbEHcnVcKixnXvzNJCfH7nfrEsh0N3vb6MTkfWJfXtQN/vfUivj73Lf79L6tZtnU/P7n6PFJbyHBUEammI/0Y21h8lN2HTzJpYMvv2qkpo30yj35+NN+6bAD/985urvzv11m/pzTeZYlIPSj0Y+z0UM1JLfwk7tmEQsZtl+TxxJcu4GhZBVc9uJjHl21Xd49IC6HQj7FFhSUM7Npyh2pG64K+nZn/9fGM7pPB9/6yhs/NWa7RPSItgEI/ho6WVbBi24FWe5RfU2ZqGx79/Gh+NG0oK7cf5GM/f5Unl+skr0hzptCPoSXhoZotfXx+fYRCxg0X5vLCNyZwXs9OfPfPq/ncnOUUHdTc/CLNkYZexNCid0ton5xAfu+MeJdyTho68ufxL47h8eU7+K/565ly/6t8bXIeN13Uh+REHVuINBf63xgj1UM1SxjXPzOwIRcKGTdc0JsXvzmB8XmZ3Pv8Bj7+y9d0K0aRZiSY6dQINhUfZdehE61yqGZ9ZaenMPtz+Tw8I5+Tpyq57rdL+eZTb1NcejLepYkEnkI/RhYVVk8JHaT+/LpMHtyVl745ka9e0p+/rdrDpJ8u4lcvb9SsnSJxpNCPkUXvFpPXJZWeaa17qGZ9tUtO4I7LBr7f5fPTF9/lkp8u4q9v79IoH5E4UOjHwLGyClZsPRiYoZoNkZvZnoduyGfuzAvISE3m63Pf5upfL1F/v0gTU+jHwJLN+ymvrFJ/fhQu6NuZebdexE8/NZz3Dp/kut8u5YaHl/HOzkPxLk0kEBT6MbCosJiU5IRWM6tmYwuFjGtGZbPo25P4j38ZzNrdpUx7cDFf+kMBG97TXD4ijUmhf47cnUWFJYztl0mbxIR4l9OitE1K4Ivj+/Lqv13MHVMGsHTLfqb+4jVm/qFAR/4ijUQXZ52jzSXVQzW/MqlfvEuJu4Ze1HX9mBy+OjmPGy7sze8Xb+ORJdt4cd1ixudlcsuk/lzQNwMzi3G1IsGkI/1zdHqopk7inru0lGS+OWUAi++8hO9ePoj1e45w3W+XcvWvlzDvnd2cqqyKd4kiLZ6O9M/RosIS+ndJJTs9Jd6ltBqpbRK5eWI/ZozN5ZmCnTz8+la+9uRbdOvYls+N7c115+eQ3j453mWKtEg60j8Hx8oqWL71AJMG6Ci/MbRNSuCGC3N5+Y5JPDwjn35d2jPr+UIuvGcB3/3zajbuPRLvEkVaHB3pn4M3NFSzSYRCxuTBXZk8uCsb3ivlkcXb+PObRTy5fAfj8zL5/LhcJg3oQiikfn+RuuhI/xws2LCX1DaJnN9HQzWbyqBuHbnnk8N447uT+fbHBvLu3iN84ZECLvnZIua8vpUjJ0/Fu0SRZk1H+g1UVeUsWF/MhAEaqnmuGjrq59aL+zNzQl+eX/MejyzZxt3PreNnLxZyzahsPjc2l35ZqTGuVKTlU+g30Jrdhyk+UsbkQV3jXUqgJSWEuGJ4D64Y3oNVRYd4ZMk2nly+k0ff2M7EAVncOC6XiXlZ6voRCVP3TgP9Y30xIYOLB6k/v7kYlp3G/Z8eweI7L+H2KQNYt6eUz/9+BZPvf4U/Lt2u2T1FUOg32IL1e/loTjoZGjrY7GR1aMPXJuex+DuX8MD0EXRsl8T3/3cNF927kF8v2kSp+v0lwKLq3jGzqcADQALwO3e/p8bzbYA/AKOA/cC17r7NzHKB9UBhuOlSd/9ybEqPnz2HT7B2dynfmToo3qUEWrTnAj49Kpvze6fzyrslzHq+kF8u2MgXxvXhCxf1ITO1TSNXKdK81Bn6ZpYAPAhMAYqAFWY2z93XRTS7CTjo7v3NbDpwL3Bt+LnN7j4ixnXH1T/WFwMwZYi6dloCM6NvVip9s1LZdfAEr7xbzG9e2czDr2/l2vN7MXNCX11cJ4ERTffOaGCTu29x93JgLjCtRptpwKPh5WeBydaKJ0v5x7q99O6cotEhLVDP9HZcP6Y3/7h9ItNG9ODJ5Tu4+KeL+N5fVrPn8Il4lyfS6KIJ/Z7AzojHReF1tbZx9wrgMNA5/FwfM3vLzF4xs/G1vYCZzTSzAjMrKCkpqdcP0NQOHz/Fks37mDq0myYBa8H6ZaUy65rhvPLti7n2/F48XbCTibMW8cN5a3UvX2nVogn92pKt5n3uztZmD5Dj7iOB24EnzKzjBxq6z3b3fHfPz8pq3lMavLR+L6cqncvP6x7vUiQGeqS148dXncfLd0zi6pE9+ePS7Uy4byE/+ds69h8ti3d5IjEXTegXAb0iHmcDu8/WxswSgU7AAXcvc/f9AO6+EtgMDDjXouNp/uo99Exrx/DsTvEuRWKoV0YK914zjJfvmMjHz+vOw69vZfyshcx6fgMHj5XHuzyRmIlm9M4KIM/M+gC7gOnA9TXazANmAG8A1wAvu7ubWRbV4V9pZn2BPGBLzKpvYqUnT/HaxhJuHJurrp0W7sNG/uT3ziAnI4WXNxTzm0XVJ3zH9c9kXL9MbhrfpwmrFIm9OkPf3SvM7DbgBaqHbM5x97VmdjdQ4O7zgIeBP5rZJuAA1b8YACYAd5tZBVAJfNndDzTGD9IU/rFOXTtB0aVDW6afn8OkgSdZsH4vL28oZsnmfRwvr+DGcbl0aJsU7xJFGsTca3bPx1d+fr4XFBTEu4xaffHRAtbtPsziOy+p95F+Q+eXkeZh96ETLNhQzPo9paSlJDFzQl9mXJhL+zaayUSaBzNb6e75dbXTFblRKj15ilc3ljD1I93VtRNAPdLaccMFvZl32zhG9kpj1vOFjJ+1kNmvbuZEuaZ3kJZDoR+l+av2UF5RxRXD1bUTZMOy0/j950fz51vGMrRHR/7f/A2Mn7WQh1/fqrl9pEVQ6Efp2ZVF9O+SyoheafEuRZqBj+ak88ebxvDMly9kQNdUfvTcOibMWsijS7ZRVqHwl+ZLoR+FrfuOUbD9INeMylbXjvyT83MzeOJLF/Dkly4gt3N77pq3lkn3LeLxZdspr9CN3KX5UehH4U8riwgZXD2y5oXIItUu7NeZp26+gMduGkP3Tm353l/WcMnPFvF0wU4qKhX+0nxo6EEdKqucP71ZxIQBWXTt2Dbe5UicRTMK65MfzWZYdhovrdvLvz27inv/voHvf2IIVwzvQYJu5iJxpiP9OizZvI89h09yzajseJciLYSZMaBrB26Z1I/PjulNUkKIbzz1NlN/8SrzV++hqqp5DZOWYNGRfh0eW7qdTu2SuHSwboso9WNmDOnRkUHdO5CWksTPX3qXWx5/k8HdO3LHlAFMHtxF54ikyelI/0Ns33+MF9ft5TNjcmibpJufS8OEzPjEsB68+M2J3P/p4Rwvr+CLfyjgql8v4ZV3S2huF0hK66Yj/Q/x+8XbSAwZM8bmxrsUaeEizwV88aK+vLXjIC9vKGbGnOX07pzClMFd6VvL/RmuH5PTlGVKACj0z+LwiVM8U7CTTwzroRO4ElMJISM/N4MRvdJYsf0giwqL+d3rW+mb1Z7Jg7rSJ7N9vEuUVkyhfxZPrdjBsfJKbrpIsypK40hMCHFh387k905n2dYDvPpuCb99bQv9stpz6eCu9O6s8JfYU+jX4uSpSn6/eBtj+mTwkZ6aN18aV1JCiIv6ZzI6N4NlW/fz6sZ9PPTqFvp3SWVgtw6M6p0e7xKlFdGJ3Fr84Y1t7Dl8kq9Pzot3KRIgyYkhxudl8e3LBnL5R7qx59AJPvmbJXxuznKWbdmvE74SEzrSr+HQ8XJ+9fImJg7IYmz/zHiXIwF0OvzH9OnMyYpKZr+6hWtnL2VI9458flwuVwzvodFk0mA60q/h14s2c6SsgjsvHxTvUiTgkhNDfHliPxZ/5xL+61/Po6Kqim8/u4px97zM/S8WsmP/8XiXKC2QjvQj7DxwnEeWbONfR2YzuPsH7t8uEhftkhO4bnQO08/vxZLN+/n94q3898JN/PLlTQzvlcYVw7rzL8O6071Tu3iXKi2AQj+sqsr5t2dXkRQy7risRd+7XVopM6u+V2//TIoOHudvq/bw3Ko9/Phv6/nx39aT3zudi/IyubBvZ0bkpNEmUV1A8kEK/bA5i7fyxpb93PvJ8+iRpiMmad6y01O4eWI/bp7Yj637jvHcO7t5Yd17PLBgI7/4x0baJIYY1Tud0X0yGNqjE0N7dKR7p7aa9kEU+gDv7j3CrBcKuXRwFz6d3yve5YjUS5/M9nx1ch5fnZzH4eOnWL7tAG9s3s8bW/bzwIKNnB70k56SVD0XULeODOzWgYFdO5DXNZWUZMVAkAT+X/vAsXK+8thKOrRJ5L/+dZiOhKRZiWYq59r075LKD64YwrGyCja8d4R1uw+zbk8pa3eX8tjS7ZSFb/BiBr3SU97/JTCgWwc2FR8lMzWZxFD9xnloyoiWIdChf6ysgs8/soKigyf4401jyOrQJt4licRM5C+MhFCI83qmcV7PNKrcOXCsnL2lJ8NfZbyz8xAL1u/l9KzPCWZ069SW7PR29MpIITu9HZmpbQjpoKjFC2zoHyur4MuPrWTNrsM89NlRjO6TEe+SRJpEyIzM1DZkprZhaI8zV5xXVFZRcrSMvaVlvHf4JEUHj/PWzkMs23oAgLZJIXIyUujduT29O6eQnZZCcqJGfbc0gQz9bfuOcfMfV7Kx+AizrhnOpUM0V75IYkKI7p3aVQ/9DJ/aqnKn5EgZOw8cZ+fB42zff5x39+4FIGTQI60duZ3bk5ORQsmRMv213AIEKvSrqpy/vrOLu/66llDIePQLoxmflxXvskSarZAZXTu2pWvHtuTnVv81fLy8gh37j7P9wHG27z/G0i37eX3TPp5YvoPczimM6p1Bfm465+em0zczlZBuEdmsRBX6ZjYVeABIAH7n7vfUeL4N8AdgFLAfuNbdt4Wf+y5wE1AJfM3dX4hZ9VGqqnKWbN7PrBc2sKroMMOzO/Gr6z9Kr4yUpi5FpMVLSU5kUPeODApfwFhRWcXuQyfISE2mYNtBFhYW86c3iwBIS0liVE46Q3t2qj5R3DWV3Mz2JCWoWyhe6gx9M0sAHgSmAEXACjOb5+7rIprdBBx09/5mNh24F7jWzIYA04GhQA/gH2Y2wN0rY/2D1FRRWcWa3aW8UljCn94sYseB43Tr2JaffWo4V4/sqaMPkRhJTAiR07k914/JYeYEcHe27jtGwfaDrNx2kILtB1hYWPz+SeKkBKNvZiq9MtqRnZ5Cz7R2ZKe3o3taOzq3T6ZzarKGkTaiaN7Z0cAmd98CYGZzgWlAZOhPA34YXn4W+JVVj32cBsx19zJgq5ltCu/vjdiUf0bpyVM8vWInm0uOsrnkGGt3HeZYefXvlrH9OvONS/P4+HndNVGVSCOpbXjp8F5pDO+VxqnKKkqOlLG39CTF4e9rdpXy2sZ97w8fjdQ2KURGSjKdUpJJSU6gXVIC7ZITSAl/tUtKpF1yiAQzQiE78z28nBBeDr3/uLqr6v31Ecun25/ZDx9Y937byG1Pt418/Rp1nN5XyIxKdyqrqr8q3v9eRUWlc7y8giMnK0hKCDX6dO7RhH5PYGfE4yJgzNnauHuFmR0GOofXL62xbc8GV/sh3OHHf1tPRvtk+ma25+qP9mRMn86M6ZtBlw6685VIPCUlhOiR1u6M9lY8AAAGEElEQVQDV7u7OydPVXHweDmHT5ziWFkFx8orOV5WwbHyCk6UV3LoeDnFFVWcqnTKK6s4VVFV/b2y6v2/HlqLEb3S+N9bxzXqa0QT+rX1g9R8q8/WJpptMbOZwMzww6NmVhhFXbXaDrzV0I0bVyawL95FNBN6L87Qe3FG4N+L7YDdBjTsvegdTaNoQr+I9wdwAZAN7D5LmyIzSwQ6AQei3BZ3nw3MjqbglsrMCtw9P951NAd6L87Qe3GG3oszGvO9iOYU+gogz8z6mFky1Sdm59VoMw+YEV6+BnjZq2/zMw+YbmZtzKwPkAcsj03pIiJSX3Ue6Yf76G8DXqB6yOYcd19rZncDBe4+D3gY+GP4RO0Bqn8xEG73NNUnfSuAW5ti5I6IiNTOdN/NpmFmM8PdWIGn9+IMvRdn6L04ozHfC4W+iEiA6LI4EZEAUeg3MjObamaFZrbJzO6Mdz3xZGbbzGy1mb1tZgXxrqepmdkcMys2szUR6zLM7CUz2xj+nh7PGpvKWd6LH5rZrvDn420z+3g8a2wqZtbLzBaa2XozW2tmXw+vb5TPhkK/EUVMYXE5MAS4Ljw1RZBd7O4jAjo07xFgao11dwIL3D0PWBB+HASP8MH3AuDn4c/HCHef38Q1xUsFcIe7DwYuAG4N50SjfDYU+o3r/Sks3L0cOD2FhQSQu79K9ei2SNOAR8PLjwJXNWlRcXKW9yKQ3H2Pu78ZXj4CrKd65oJG+Wwo9BtXbVNYNMo0FC2EAy+a2crwVdgCXd19D1T/5we6xLmeeLvNzFaFu38C0dUVycxygZHAMhrps6HQb1xRTUMRIOPc/aNUd3fdamYT4l2QNCu/AfoBI4A9wM/iW07TMrNU4E/AN9y9tLFeR6HfuKKahiIo3H13+Hsx8Bequ7+Cbq+ZdQcIfy+Ocz1x4+573b3S3auA3xKgz4eZJVEd+I+7+5/Dqxvls6HQb1zRTGERCGbW3sw6nF4GLgPWfPhWgRA5hckM4K9xrCWuTgdc2NUE5PMRnob+YWC9u98f8VSjfDZ0cVYjCw87+wVnprD4SZxLigsz60v10T1UT//xRNDeCzN7EphE9QyKe4G7gP8FngZygB3Ap9y91Z/gPMt7MYnqrh0HtgE3n+7Tbs3M7CLgNWA1cPrmAv9Odb9+zD8bCn0RkQBR946ISIAo9EVEAkShLyISIAp9EZEAUeiLiASIQl+kCZnZiKDMHinNk0JfAiU882k8jQAU+hI3Cn1pNcws18w2mNmj4Um7njWzlPA8/j8ws9eBT5lZPzN7Pjzx22tmNii8fT8zW2pmK8zsbjM7Gl4/ycwWhfe3wcweD19FSXi/K8xsjZnNjli/yMzuNbPlZvaumY0PX5V9N3BteL74a81sYsT88W+dvmpZpLEo9KW1GQjMdvdhQClwS3j9SXe/yN3nArOBr7r7KOBbwK/DbR4AHnD38/ngHEkjgW9QfV+EvsC48Ppfufv57v4RoB3wiYhtEt19dHi7u8LTa/8AeCo8X/xT4de/1d1HAOOBE7F5G0Rqp9CX1manuy8OLz8GXBRefgren8lwLPCMmb0NPAScnvPlQuCZ8PITNfa73N2LwpOBvQ3khtdfbGbLzGw1cAkwNGKb0xNnrYxoX9Ni4H4z+xqQ5u4V0f6gIg2RGO8CRGKs5rwipx8fC38PAYfCR9b1URaxXAkkmllbqv9KyHf3nWb2Q6BtLdtUcpb/a+5+j5n9jep+/qVmdqm7b6hnbSJR05G+tDY5ZnZhePk64PXIJ8PzlG81s09B9QyHZjY8/PRS4JPh5elRvNbpgN8X/gvimii2OQK8329vZv3cfbW73wsUAIOi2IdIgyn0pbVZD8wws1VABtU35qjpM8BNZvYOsJYzt7D8BnC7mS2nusvn8Ie9kLsfonre99VUz5a5Ior6FgJDTp/IBb4RPgn8DtX9+X+PYh8iDaZZNqXVCN9q7rnwSdWGbJ8CnHB3N7PpwHXurnsaS6uiPn2RM0YBvwoPuzwEfCHO9YjEnI70RUQCRH36IiIBotAXEQkQhb6ISIAo9EVEAkShLyISIAp9EZEA+f+ZEj+nrr9YbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f33372b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6a2f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B+6xy1leLyI8kXscyMGYU7x73t0eUzpkZjdYBZxXnkJHs5e0jzW6HYuLcmElCRLzAfcC1wArgFhFZEVLsNuC0qlYA9wL3OOubgY+q6tnArcCjQfvcD9wOVDqPaybxPoxxxY7aNpK8wvzcmZUkAu0Sdr+EGUs0VxJrgGpVPaKqA8ATwPqQMuuBR5zlp4GrRERU9V1VrXfW7wFSnauO+UC2qr6tqgr8Arhh0u/GmGm2o/Y0y+dnk+SdeTW3Fy3Jp7qxi6ZOa5cwkUXzl10M1AY9r3PWhS2jqkNAO5AfUuYvgXdVtd8pXzfGMY2JayMjyp4THZxTkuN2KBMSuF/CusKa0USTJMK1Feh4yojImfiroP56HMcM7Hu7iFSJSFVTU1MU4RozPY639tDZP8TZxTMzSZy1IJvMFB9vWVdYM4poOnbXAaVBz0uA+ghl6kTEB+QArQAiUgI8A3xGVQ8HlS8Z45gAqOqDwIMAq1evDptIjJlqj205/mfr3qvzN1rXtvayYIa1SQD4vB4uXJzHm9XWeG0ii+ZKYhtQKSLlIpIMbAA2hpTZiL9hGuBG4CVVVRHJBZ4D7lLVNwOFVbUB6BSRC51eTZ8Bfj3J92LMtKpv68XrEYqy43vk19FcWlnI8dYejjlDnRsTaswk4bQx3AFsBvYBT6nqHhG5W0Sud4o9BOSLSDXwdSDQTfYOoAL4pojscB5FzrYvAT8FqoHDwPOxelPGTIf6tj7mZafi88y8RuuASysLAHjtkF1NmPCiGkdAVTcBm0LWfStouQ+4Kcx+3wa+HeGYVcBZ4wnWmHihqpxo6+Ws4my3Q5mU8oIMinPTeONQE5++cJHb4Zg4NHO/AhnjorbeQXoHh2dkW0QwEeHSygLeqm5haHjE7XBMHLIkYcwE1Lf1ArAgZ2YnCfC3S3T2D7HTaYg3JpglCWMmoL6tF4/AvJxUt0OZtHUV+YjA69YuYcKwJGHMBNS39VGUlToj77QOlZuezDnFOZYkTFgz/y/cGBfUt/WyIHfmX0UEXFpZyI7aNtp7Bt0OxcQZSxLGjFNH3yCd/UMzvtE62BXLChkeUd6wG+tMCEsSxoxT/enZ02gdsLI0l+xUH68caHQ7FBNnLEkYM04n2nsRYP4sqm7yeT1curSQVw824R+Y2Rg/SxLGjFN9Wx8FmSmk+LxuhxJTVywtpLGzn70NHW6HYuKIJQljxmm2NVoHXL6sEIBXDthoy+aPLEkYMw5d/UO09w7OqkbrgKKsVM5ckM2rliRMEEsSxoxDQ+BO61mYJMDfy2n78dO091pXWOMX1QB/xhi/2TQcR7DAfBmDQ8rwiPLd5/f/yWRKn1i70K3QjMvsSsKYcTjR3kdeRjJpybOr0TqgNC+d1CQPB091uh2KiROWJIwZh/q2XhbMgvGaIvF6hIqiLA6d6rSusAawJGFM1HoHhmntHpi17REBy+Zm0tE3xMmOPrdDMXHAkoQxUapvn92N1gGVc7MAOHjSqpyMJQljolY/y3s2BWSnJjE/J5UDp7rcDsXEAUsSxkSpvq2XnLQkMlNmf6fApXOzON7aTd/gsNuhGJfN/r92Y2Kkvq1v1l9FBCydm8WrB5uobuzirOKc97vIRmJdZGcvu5IwJgr9Q8M0d/XPyuE4wlloXWGNI6okISLXiMgBEakWkTvDbE8RkSed7VtEpMxZny8iL4tIl4j8OGSfV5xj7nAeRbF4Q8ZMhZPtfShQPMtuoovE6xEqCjM5aF1hE96YSUJEvMB9wLXACuAWEVkRUuw24LSqVgD3Avc46/uAbwL/EOHwn1TVlc7DBrI3cetEgjRaB1s6N8u6wpqoriTWANWqekRVB4AngPUhZdYDjzjLTwNXiYioareqvoE/WRgzY9W39ZGZ4iMrNXGa8ZYGusJaL6eEFk2SKAZqg57XOevCllHVIaAdyI/i2D9zqpq+KSISRXljXBEYHjyR/kyz0/xdYa1dIrFFkyTCfSpCKymjKRPqk6p6NnCp8/h02BcXuV1EqkSkqqnJhjA2069vcJjGzsTp2RRs6dwsjrVYV9hEFk2SqANKg56XAPWRyoiID8gBWkc7qKqecH52Ao/hr9YKV+5BVV2tqqsLCwujCNeY2Np/spMRheIETRIjCoebrMopUUWTJLYBlSJSLiLJwAZgY0iZjcCtzvKNwEs6SpcIEfGJSIGznARcB+web/DGTIfdJ9qBxGq0DliYl06Kz7rCJrIxW+FUdUhE7gA2A17gYVXdIyJ3A1WquhF4CHhURKrxX0FsCOwvIjVANpAsIjcAVwPHgM1OgvACvwN+EtN3ZkyM7KlvJy3JS25aktuhTDv/qLCZHDzVhaomVJuM8Yuqq4aqbgI2haz7VtByH3BThH3LIhx2VXQhGuOu3Sc6KM5NS9h/kMvmZrGnvoNTnf3My06MmwnNH9kd18aMYmBohAMnO5mfIHdah2OjwiY2SxLGjGJfQwcDwyOUzEl3OxTX5KQlMS/busImKksSxoxiR20bAKVzEq/ROpi/K2wP/dYVNuFYkjBmFDtq2yjKSiEnARutgy2dl8mwqnWFTUCWJIwZxY7aNlaW5iZso3XAorwMUnwem4goAVmSMCaC090DHG3uZuXCXLdDcd0fu8LaqLCJxpKEMRHsqPO3R6wstSQB/naJ9t5BGjv73Q7FTCNLEsZEsON4Gx6Bc0osSUDwqLDWyymRWJIwJoIdtW0snZuVEHNaRyMnLYmirBSqG61dIpFYkjAmDFV9v9Ha/NGSwkxqWroZGhlxOxQzTSxJGBPG0eZu2nsHLUmEWFKYweCwUtfa63YoZppYkjAmjMBNdNaz6U+VF2QiwOFmq3JKFJYkjAljW00rWak+Kouy3A4lrqQle1mQm8bhxm63QzHTxFrkYuSxLcdH3f6JtQunKRITC1uOtLKmLA+vJ7FvogtnSWEGb1a3MDA0QrLPvmfOdvYbNiZEY0cfR5q7uXBxNNO0J57Fhf4hOo612tVEIrAkYUyId476Z95duzjP5UjiU1l+Bh6BI02WJBKBJQljQrxzpIXMFB8r5me7HUpcSvZ5KM1Lt8H+EoQlCWNCbDnSwgVlc/B57eMRyZLCTE6c7qV3wIYOn+3sU2BMkKbOfg43dbPW2iNGtbgwAwVqWqzKabazJGFMkC1HWwBYW27tEaNZOCedJK9YlVMCsCRhTJAtR1rJSPZyVnGO26HENZ/Xw6L8DGu8TgBRJQkRuUZEDohItYjcGWZ7iog86WzfIiJlzvp8EXlZRLpE5Mch+6wSkV3OPj+SRJ/VxcSFtw43s6osjyRrjxjTkoIMTnb00dU/5HYoZgqN+UkQES9wH3AtsAK4RURWhBS7DTitqhXAvcA9zvo+4JvAP4Q59P3A7UCl87hmIm/AmFipbe3hcFM3l1UWuB3KjLC4MBOAI1blNKtFc8f1GqBaVY8AiMgTwHpgb1CZ9cA/O8tPAz8WEVHVbuANEakIPqCIzAeyVfVt5/kvgBuA5yfxXoyZlJf2NwJw5RlFLkcSe2ONCDARC3LTSPF5OGxVTrNaNNfUxUBt0PM6Z13YMqo6BLQDo3UPKXaOM9oxjZlWL+1vpLwg4/1vyGZ0Xo9QXpBhVxKzXDRJIlxbQegkt9GUmVB5EbldRKpEpKqpqWmUQxozcT0DQ7x9pIUPLJt9VxFTaUlhJi3dA5xos6HDZ6tokkQdUBr0vASoj1RGRHxADtA6xjFLxjgmAKr6oKquVtXVhYWFUYRrzPi95QxYNxurmqbSEueq6+3DLS5HYqZKNEliG1ApIuUikgxsADaGlNkI3Oos3wi8pKoRryRUtQHoFJELnV5NnwF+Pe7ojYmRlw40kpHsZY3dHzEuRdkpZCR7eau62e1QzBQZs+FaVYdE5A5gM+AFHlbVPSJyN1ClqhuBh4BHRaQa/xXEhsD+IlIDZAPJInIDcLWq7gW+BPwcSMPfYG2N1sYVqsrL+xu5pLLAhr4eJ48IiwszeetwC6qK9WSffaKaT0JVNwGbQtZ9K2i5D7gpwr5lEdZXAWdFG6gxU2VfQycN7X387V9Uuh3KjLS4MINdJ9o52txtjf6zkH1tMgnv2ffq8XqEq5bPdTuUGSnQLvGWtUvMSpYkTEJTVX7zXj3rKgooyExxO5wZKT8jmQU5qdZ4PUtZkjAJ7d3aNmpbe7n+3AVuhzJjiQgXLSngrcPNjIyM1vPdzESWJExC27ijnmSfhw+daVVNk3HxknxO9wyy/2Sn26GYGIuq4dqY2Wh4RHn2vQauXFbEb3Y2uB3OjHbREv8AC28dbmbFApvRbzaxKwmTsN4+3EJzVz/Xr7SqpslakJtGeUGGtUvMQnYlYRJCuAHunt5eR7LPQ1Nnvw0NHgMXLcln4456hoZHbOrXWcR+kyYh9Q0Os+tEG+cU51iCiJF1Swro6h/ivRPtbodiYsg+HSYh7ahtY3BYbRiOGLpwsf9cWpXT7GJJwiQcVWXr0VYW5KRSnJvmdjizRn5mCmfMy+KtwzaO02xiScIknLrTvZzs6OOC8jwbayjGLl5SQFXNafoGh90OxcSIJQmTcLbVtJLkFc4tyXU7lFnn4iX59A+N8O7xNrdDMTFiScIklN6BYXbWtXFOSS6pSV63w5l11izOwyNYldMsYknCJJSqY60MDisXLR5tdl0zUdmpSZxTkmuD/c0iliRMwhgeUd4+0kJZfgYLrMF6yly8JJ+dtW109Q+5HYqJAUsSJmHsa+igrWeQi5fYVcRUuqSygKER5Y1DNif9bGBJwiSMtw63MCc9ycYWmmJryvLISUvixT2n3A7FxIAlCZMQ6tt6qWnp5qLF+Xis2+uU8nk9XLW8iN/vb2RoeMTtcMwkWZIwCeH1Q00k+zysWmR3WE+Hq1fMo713kK01rW6HYibJkoSZ9Wpbe3ivrp01ZXmkJVu31+lw2dICUnweq3KaBSxJmFnvJ68fwSPCuooCt0NJGOnJPi6tLOC3e0+harPVzWRRJQkRuUZEDohItYjcGWZ7iog86WzfIiJlQdvuctYfEJEPBa2vEZFdIrJDRKpi8WaMCdXS1c9TVbWsXJhLTlqS2+EklKtXzONEWy976jvcDsVMwphJQkS8wH3AtcAK4BYRWRFS7DbgtKpWAPcC9zj7rgA2AGcC1wD/7hwv4AOqulJVV0/6nRgTxiNv1dA/NMKllXYVMd2uWl6ER2DznpNuh2ImIZoriTVAtaoeUdUB4AlgfUiZ9cAjzvLTwFXiHzltPfCEqvar6lGg2jmeMVOuu3+IR94+xgeXz6UoK9XtcBJOfmYK6yoKeObdE4yMWJXTTBXNzHTFQG3Q8zpgbaQyqjokIu1AvrP+nZB9i51lBV4UEQUeUNUHxx++MZE9vvU47b2DfPGKJexv6HQ7nFkt3Mx/APNz0nj9UDNbjra+Pw+2mVmiuZII16k89GtBpDKj7btOVc/HX431FRG5LOyLi9wuIlUiUtXUFF93cPYPDfPrHSf4zMNb+c179fTb8MhxY2BohIfeOMqa8jzOXzjH7XAS1or52aT4PPxye+3YhU1ciiZJ1AGlQc9LgPpIZUTEB+QAraPtq6qBn43AM0SohlLVB1V1taquLiwsjCLc6bH7RDvrvvsSf/PEDqpPdfLO4RZ+9NIhjjR3uR2aATburKehvY8vXb7E7VASWrLPwzklOTy/66SN5TRDRZMktgGVIlIuIsn4G6I3hpTZCNzqLN8IvKT+fm8bgQ1O76dyoBLYKiIZIpIFICIZwNXA7sm/nenR3T/EVx9/F5/Hw6O3reGN/3klX7h0MSLCw28cpaG91+0QE9rIiPLAq4c5Y14WVyyLny8WiWrVwjn0Dg6zaVeD26GYCRgzSajqEHAHsBnYBzylqntE5G4Rud4p9hCQLyLVwNeBO5199wBPAXuBF4CvqOowMBd4Q0R2AluB51T1hdi+tanz/27cw7GWbn6wYSWXVhbi8QhlBRl8+fIlpCZ5/Q111jfcNS/uPcWhxi6+ePkSm3kuDpTmpbO4IIOnq+rcDsVMQDQN16jqJmBTyLpvBS33ATdF2PdfgX8NWXcEOHe8wcaDjTvreXp7HV+7soILQ+YkSE/xcd0583mqqo53jrRw8RLrdjndRkaUH/zuIOUFGVx3zny3wzFdr8ATAAATtUlEQVSAiHDzBaV85/n97Kpr5+ySHLdDMuNgd1yPQ//QMN/ZtI9zS3L42lWVYcucW5JLZVEmL+49RVvPwDRHaF7ce5L9Jzv52lUV+Lz25x0vblm7kKwUH/e/Wu12KGac7FM0Dk9V1dHQ3sc/fGhZxH9AIsL6lcWMjCgvH2ic5ggTm/8q4hCLCzO4/tzisXcw0yY7NYnPXLyI53ef5HCTde6YSSxJRKl/aJj7X67m/IW5XDLGGEB5GcmsWjSHPxxrs6uJafTCHv9VxN9cVYnXY20R8eZz68pJ9np44NXDbodixsGSRJR+WVVHfXsff/sXS6NqDL1saSGK8vohmxB+OgwOj/C9zQeoKMrkunMWuB2OCaMgM4UNF5TyzLsnqG+zHoAzhSWJKAwMjXD/K4c5b2Fu1GMAzUlP5ryFc9hW00pn3+AUR2ge23KcI83d3HXtGXYVEcdud3qc/e9N+9wOxUTJkkQUnn2vnhNtvXztyspxdam8fGkhwyPKm9V2NTGVOvoG+cHvDnLR4nyuPKPI7XDMKIpz0/jKFRU8+14Drx6MrxEUTHiWJMagqjz42hEqizLHfWNWQWYKZ5fk8M7RVk53W9vEVLnv5Wraegf5xkeW230RM8AXr1jM4oIMvvmr3fTZUDZxz5LEGN6obmb/yU6+cNniCf0DumJZEQNDI/zsrZrYB2c43NTFz96o4WPnFXNWsfW/nwlSfF6+/bGzON7aww9/f8jtcMwYLEmM4cHXjlCYlcL6lRNrDJ2XncqK+dn8/M2j1jYRYyMjyl3/dxdpyV7uvPYMt8Mx43DxkgJuXl3K/a8c5nkbriOuWZIYxb6GDl4/1MxnLy4jxTfxuZE/sKyIjr4hHn3nWAyjM49tPc7Wmla+8ZHlNl/EDPQv68/k/IW5/N1TO9h9ot3tcEwEliRGcd/L1WQke/nU2kWTOk7xnDQuX1rIQ68fpXfA6mBjoaG9l+8+v591FfnctKrE7XDMBKQmeXng06vJz0jhrx6pspvs4lRUYzclokOnOnluVwNfunwJOemTnxv5q1dWcON/vM3jW4/z+UvKYxBh4hocHuGrj73L8Ijyvz92NiIScdIbEx9G+/18/PxiHttynI//+1s88OlVfzYmmnGXXUlE8KOXqklL8vJXly6OyfFWl+Vx4eI8HnjtMP1DdjUxGd/ZtJ+qY6e558ZzWJSf4XY4ZpLm56Txq6+soyAzmU8/tIWfvn6EweERt8MyDksSYRw61cmz79Vz68Vl5GUkx+y4d3ygklMd/Ty93YZMnqjf7Kzn4TeP8tmLy7j+XLuzerYozUvnv7+0jksrC/n2c/v4yI9e5+UDjTY3dhywJBHGD39/iLQkL1+I0VVEwLqKfFaW5nL/K4ftm9IEvHawib9/aierFs3hf314udvhmBjLSU/ioVtX8+CnV9E7OMznfraNS/+/l/n+iwfYerTV7qlwibVJhHirupln32vgq1dWxPQqAvwjxH71ygpue6SKX717gptWl469kwH8v5cv/KKKJUWZPHTrapJ99v1mNhIRrj5zHpctLWTznpM8vb2O//NyNT96qRqP+G9QnZOeTG56EnPSk5mTkcyc9CRy05PJSPaOei/TJ9YunMZ3MntYkgjSNzjMN361m0X56XzlAxVT8hpXnlHEOSU53PPCfq48o4j8zJQpeZ3Z5IXdJ/m7J3dQlp/Bf/3VWnLTY5u8TfxJTfKyfmUx61cW09LVzw9+d4jjrT00dfbT1jPA8dYeekOuLFKTPJTkplMyJ42KokwW5WfYOF4xYEkiyH0vV3O0uZv/vG0tqUkTvy9iNCLCv914Lh/9P2/wjWd2c/+nzrehJCIYHlHu/e1BfvxyNSVz0vj4+cW8sPuk22GZKTBW77Tl87NZPj/7T9b1DQ5zumeAtp5BTvcMcKqjnxOne3jtUBOvHGwixedh+fxsVi2aQ3mBdXCYKEsSjh21bfzHq4f5+HnFXBLlSK8TtWxeFl+/einffX4/v95Rzw3n2QQ5ofaf7OCbv9rNtprT3Ly6lBULskmymeZMkNQkL/Nz0pifk/Yn6/sHhznc1MX+k53srm9nR20beRnJtPUM8JerSv6svBmdJQn84/98/ufbmJeTyj9dt2JaXvMLly7md3tP8c1f7aa8IINzS3On5XXj3cn2Ph547TC/ePsY2ak+vnfTufzl+cU8vrXW7dDMDJGS5GXFghxWLMjho+cuYE99O1U1p/neiwf5/m8PcvnSQjasWciVZxTZF48oJHySONXRx2ce2ooAj35+bcwbqyPxeoQf3nIeGx58m089tIVffH4N5y2cMy2vHW+GR5RtNa08VVXLxh31jKhy8wWl/OOHzmDONP0+zOyU5PWwsnQOK0vnsK4in19W1fFUVS1//eh2CrNSuGlVCRsuWMjC/HS3Q41bojp2P2QRuQb4IeAFfqqq3w3ZngL8AlgFtAA3q2qNs+0u4DZgGPiaqm6O5pjhrF69WquqqqJ+c2N55UAj//j0e3T3D/HE7RdxdsnERxEdq041Us+KE229fOIn79DaNcC/3XQOHzpzXly2UUz0/YUzODzCsZZu/nC8je01p/n9/kaau/pJT/byP1aXctsl5ZTm/emH1u6oNpMV+BsdGh7h5QNNPLH1uP9eDIWVpblcc9Y8PrCsiMqiTDyzrMFbRLar6uoJ7TtWkhARL3AQ+CBQB2wDblHVvUFlvgyco6pfFJENwMdU9WYRWQE8DqwBFgC/A5Y6u416zHBilSQOnurkodeP8mRVLZVFmdx788pJDzM9mX+iDe29fP7nVexr6ODSygL+14eX/1kj3VQbHB6hpWuA5q5+TvcM0Nrtf5zuHqCle4CdtW30DA4zPKwMjSjDI/6fqorHIxRkppDkFZK8Hnwe/88kr+BzfvYODNPRN0RTZz8n2noZdm6SyklLYl1FPh8+ez5XnlFEenL4i1tLEmaywn0GG9p7+e8/nGDznpO8V+cfZDAwR/0Z87KoKMqkZE46c7NTyMtIJtXnnZEJZKqTxEXAP6vqh5zndwGo6neCymx2yrwtIj7gJFAI3BlcNlDO2W3UY4Yz0SRxrKWbPfUd7Klv543qFnbWtuHzCJ9bV8bfX70sJj2ZJvtNe2h4hEffOcb3XzxIZ/8Qiwsz+OCKuayYn82SwkyKslJIS/aSnuwbtVvfiPPPu3dgmM7+Qbr6h+jqG6Kjb5BmJwk0d/p/NnX2+5939XO6J/ww5gKkJ3tJT/GRnuTF5xV8Hg9ej+D1CCL+15yfm8bg8IjzUAaHRxhyfg4Oj9A7OExqkpeMZB8FmcnkZ6RQMieNgqwUPHF45WQSz+XLCnmrupktR1v5w/HTHGvpef/LTLDAl6Fkr+f9L0NJXg8L89NJTfKSmuQlLclDerKPrNTAI+lPfmYHLaf4vGN21R0ZUUSYcC3DZJJENG0SxUBwq2EdsDZSGVUdEpF2IN9Z/07IvoGuPGMdM2ZufXgrNS09eD3C8vlZ/NNHlnPDecUUxNE9Cj6vh8+tK2f9ymKe29XAC7sb+OnrR8P+kab4PKQne1EI+WY/QjSjGGQkeynISqEwM4UlhZmsXZxHQ3sfmSk+MlN8ZCT7yEjxkZHsJTXZG9U/8bGSoF0JmHhXnJvGTatL37/JtX9omKPN3TS099HY0cdL+5v8X3qGRhhwvvwMDCtDw/7nXf3+K+X+oRF6B4bpHhiiq3+IKGr0EeH9xOP1yPtf9oI/1we+fc2kpiyYqGiSRLj/EKFvO1KZSOvDdSkIeypF5Hbgdudpl4gciBBnVI4AzwFfmMxBwisAIk5m/cnYv95EjRrnRE3B+5uSOKeAxRlbrsU5zr/haY8z9Z4J71oATHi+g2iSRB0QPH5ECVAfoUydU92UA7SOse9YxwRAVR8EHowiTleJSNVEL+emk8UZWxZnbFmcsefEWjbR/aPpJLwNqBSRchFJBjYAG0PKbARudZZvBF5Sf2PHRmCDiKSISDlQCWyN8pjGGGNcNuaVhNPGcAewGX931YdVdY+I3A1UqepG4CHgURGpxn8FscHZd4+IPAXsBYaAr6jqMEC4Y8b+7RljjJmMqG6mU9VNwKaQdd8KWu4Dboqw778C/xrNMWe4uK8Sc1icsWVxxpbFGXuTijWqm+mMMcYkJhu4xBhjTESWJCZJRK4RkQMiUi0id7odT4CIlIrIyyKyT0T2iMjfOOvzROS3InLI+RkXA0aJiFdE3hWRZ53n5SKyxYnzSaeDg9sx5orI0yKy3zmvF8Xx+fw75/e+W0QeF5HUeDinIvKwiDSKyO6gdWHPofj9yPlsvSci57sc5785v/v3ROQZEckN2naXE+cBEfmQm3EGbfsHEVERKXCeT+h8WpKYBGfIkvuAa4EVwC3OUCTxYAj4e1VdDlwIfMWJ7U7g96paCfzeeR4P/gbYF/T8HuBeJ87T+Mf/ctsPgRdU9QzgXPzxxt35FJFi4GvAalU9C3/nkA3Exzn9OXBNyLpI5/Ba/D0iK/HfK3X/NMUI4eP8LXCWqp6Df1ihuwCcz9UG4Exnn393/je4FSciUop/2KPgu1gndD4tSUzOGqBaVY+o6gDwBLDe5ZgAUNUGVf2Ds9yJ/x9aMf74HnGKPQLc4E6EfyQiJcBHgJ86zwW4EnjaKeJ6nCKSDVyGvycfqjqgqm3E4fl0+IA0576ldKCBODinqvoa/h6QwSKdw/XAL9TvHSBXROa7FaeqvqiqQ87Td/Df3xWI8wlV7VfVo0A1/v8NrsTpuBf4R/70JuUJnU9LEpMTbsiSuJtBSETKgPOALcBcVW0AfyIBityL7H0/wP8HPeI8zwfagj6Q8XBeFwNNwM+carGfikgGcXg+VfUE8D383yIbgHZgO/F3TgMincN4/nx9HnjeWY6rOEXkeuCEqu4M2TShOC1JTE40Q5a4SkQygf8L/K2qdrgdTygRuQ5oVNXtwavDFHX7vPqA84H7VfU8oJs4qFoKx6nTXw+U4x99OQN/VUMot8/pWOLx7wAR+Qb+6tz/CqwKU8yVOEUkHfgG8K1wm8OsGzNOSxKTE82QJa4RkST8CeK/VPW/ndWnApeYzs9Gt+JzrAOuF5Ea/NV1V+K/ssh1qkogPs5rHVCnqluc50/jTxrxdj4B/gI4qqpNqjoI/DdwMfF3TgMincO4+3yJyK3AdcAn9Y/3D8RTnEvwfznY6XymSoA/iMg8JhinJYnJidvhRZx6/YeAfar6/aBNwUOo3Ar8erpjC6aqd6lqiTO2zAb8Q7p8EngZ/xAvEB9xngRqRWSZs+oq/CMJxNX5dBwHLhSRdOfvIBBrXJ3TIJHO4UbgM06vnAuB9kC1lBvEP1Ha/wSuV9WeoE2Rhh+adqq6S1WLVLXM+UzVAec7f78TO5+qao9JPIAP4+/pcBj4htvxBMV1Cf5LyfeAHc7jw/jr+38PHHJ+5rkda1DMVwDPOsuL8X/QqoFfAilxEN9KoMo5p78C5sTr+QT+BdgP7AYeBVLi4Zzin4SsARh0/oHdFukc4q8euc/5bO3C31vLzTir8dfpBz5P/xFU/htOnAeAa92MM2R7DVAwmfNpd1wbY4yJyKqbjDHGRGRJwhhjTESWJIwxxkRkScIYY0xEliSMMcZEZEnCGGNMRJYkzIwjImURhkZ+RUQmPTm9iHxWRH482eMYMxtYkjBmCgQNfzEdrzVdw1KbBGRJwsxUPhF5xJk85WlnYLP3icgtIrLLmXTnnijWf05EDorIq/jHk4pIRH4uIv8hIq87+1znrP+siPxSRH4DvOis+39EZJsT57846zJE5DkR2enEcbOz/rsistcp+72g17ox6LW7nJ9XiH9Sqcfw3z2LiHxKRLaKyA4RecCSh4mFafu2Y0yMLcM/BMGbIvIw8OXABhFZgH+CnVX4J9d5UURuwD8kRbj1W/APY7EK/7DaLwPvjvH6ZcDl+AdUe1lEKpz1FwHnqGqriFyNfxyfNfiHRNgoIpcBhUC9qn7EiTdHRPKAjwFnqKpK0Kxno1iDfxKcoyKyHLgZWKeqgyLy78AngV9EcRxjIrIkYWaqWlV901n+T/wzsQVcALyiqk0AIvJf+CcM0gjrCVn/JLB0jNd/SlVHgEMicgQ4w1n/W1UNTAJztfMIJJxM/EnjdeB7zpXMs6r6ulM91Qf8VESeA56N4hxsVf8kN+AfxG8VsM0/ph9pxMeItGaGsyRhZqrQQceCn4cbN3+09eGON9HX7w55ve+o6gN/FojIKvwDLn5HRF5U1btFZA3+f/YbgDvwD5s+hFMt7IzoGjwvdehrPaKqd43zfRgzKmuTMDPVQhG5yFm+BXgjaNsW4HIRKXDq5W8BXh1j/RUiku/MwXFTFK9/k4h4RGQJ/tFVD4Qpsxn4vPgnfkJEikWkyKkO61HV/8Q/g9z5TpkcVd0E/C3+EWfBP4rnKmd5PZAUIZ7fAzeKSJHzWnkisiiK92HMqOxKwsxU+4BbReQB/ENM3w98FPxTYIrIXfjbFgTYpKq/Bhhl/T8Db+MfdvkPwFiNvgfwJ5i5wBdVtc+p5nmfqr7otBW87WzrAj4FVAD/JiIj+Id4/hKQBfxaRFKd2P7OOcxPnPVb8SeCbsJQ1b0i8k/421k8znG/Ahwb430YMyobKtyYcRKRn+NvS3ja7ViMmWpW3WSMMSYiq24yJgLxT3gf2j7xS1X9rAvhGOMKq24yxhgTkVU3GWOMiciShDHGmIgsSRhjjInIkoQxxpiILEkYY4yJ6P8HOqnAtuZWSx0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6d386d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6dd85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OHA6KMnl+2wEAzpoz8Ceux1VlmT6h2tcpzOyE4KAok+adbVRkRMO0upLqL5hay+Y9h/zBOzMb9RwUZdK8s42T6mupzJb2K10wtZbDXd3savMH78xsdHNQlElzaxunzBhfcv3e6xQte336ycxGNwdFGXTmenhl96FBBcX0CdVUZkWL73wys1HOQVEGm/e0090TLJpeelBkM2LO5HEeUZjZqOegKIPmnW0AgxpRQP7007b9R+jq9vdom9no5aAog96gGMyIAmDulHHkeoL12/092mY2ejkoyqB5ZxtzJtVQV13SFwYeNW/yOCD/rXhmZqOVg6IMmlvbWDTI004AU+uqGFeZ5emWfcPQKzOz8igpKCQtl7ReUrOk64psr5Z0b7J9taSGgm3XJ+XrJb2/oHyTpGckPSmpqaB8qqQHJL2U/JxybIc4vHp6gg072wd9fQJAEvOmjOMpjyjMbBQbMCgkZYFbgUuBJcCVkpb0qXY1sDciTgFuAW5O9l0CrATOBJYDf5e01+s9EXFuRCwtKLsOeDAiFgMPJuuj1tb9hznc1T2koID8dYoXdxzkcKdnkjWz0amUEcUFQHNEbIyITuAeYEWfOiuAO5Pl+4CLJSkpvyciOiLiZaA5aS9NYVt3Ah8qoY8jZkNrOzD4C9m95k+ppbsnWLfVowozG51KCYq5wJaC9ZakrGidiMgB+4H6AfYN4JeS1kq6pqDOzIjYlrS1DZhR2qGMjKHeGttr7pT8BW2ffjKz0aqU23RUpKzvTHb91Unb950RsVXSDOABSS9ExCMl9Cf/hPlwuQZgwYIFpe5Wds0725hcW0l9XdWQ9p9YU8nsSTU8sXkvsLC8nTMzK4NSRhQtwPyC9XnA1v7qSKoAJgF70vaNiN6fO4Gf8NopqR2SZidtzQZ2FutURNwWEUsjYun06dNLOIzhsWFnG6dMH0/+TNvQLG2YStOmvZ5J1sxGpVKCYg2wWNJCSVXkL0439qnTCFyVLF8GPBT5d71GYGVyV9RCYDHwmKQ6SRMAJNUBy4Bni7R1FfDToR3a8THYyQCLuaBhCtsPHPG8T2Y2Kg146ikicpKuBe4HssAdEbFO0o1AU0Q0ArcDd0lqJj+SWJnsu07SD4HngBzw6YjoljQT+EnyV3gFcHdE/CJ5yi8DP5R0NbAZuLyMx1tWe9o72dPeecxB8baFUwF47OU9zJ9aW46umZmVTUkfJY6IVcCqPmVfKlg+Qj9v6BFxE3BTn7KNwDn91N8NXFxKv0ba0ak7jjEoTp0xgYk1FazZtIc/PX9eObpmZlY2/mT2MdjQmtzxNMRbY3tlMmJpw1Qe27SnHN0yMysrB8UxaN7ZRk1lhrnJnE3H4m0NU9nY2s6uto4y9MzMrHwcFMegeWcbJ08bTyYz9Dueel2wMD9TSZNHFWY2yjgojkHzzmO/46nXW+ZOproiw2Mv7y1Le2Zm5eKgGKJDnTle3Xe4bEFRVZHh3PmTWeMRhZmNMg6KIdqYzPFUrqAAuPDketZt3c+e9s6ytWlmdqwcFEN0rHM8FbNsyUx6An713I6ytWlmdqwcFEO0obWNjOCk+vJ9QO7MOROZN2Ucq57dVrY2zcyOlYNiiJp3tnFSfR3VFdmBK5dIEpeeNYvfNu9i/+GusrVrZnYsHBRD1LyzbcjfQZFm+Vmz6eoOHnrBp5/MbHRwUAxBrruHTbuH9vWnAzlv/mRmTazhX57ZXva2zcyGwkExBJt2t9PVHSwehqDIZMTys2bx6xdbae/Ilb19M7PBclAMwQvbDwJw2qwJw9L+8rNm0ZHr4ZfPeVRhZiPPQTEE67cfJJvRsJx6gvy8T6fOHM//eqiZXHfPsDyHmVmpHBRD8ML2gzTU11JTWb47ngplM+Lzy05jY2s7P3781WF5DjOzUjkohmD99oOcPmvisD7HsiUzOWfeJL72qxfpyHUP63OZmaVxUAxSe0eOzXsODdv1iV6S+L/ffzpb9x/h+49uHtbnMjNL46AYpBd3DO+F7ELvWjyNdy+exs2/eIGHX9g57M9nZlaMg2KQ1id3PJ1+HIIC4Bsrz2PxjPF88q61ngPKzEZESd+Zba95YftBaquyzJ9Svjme0kypq+LuT1zEx+5YzSe/v5ZlS2bykQtP4u2L6skmX5h09+r0U1N/duGC49FVMxujSgoKScuBrwNZ4DsR8eU+26uBfwDOB3YDV0TEpmTb9cDVQDfwmYi4X9L8pP4soAe4LSK+ntS/AfhPQGvS/H+NiFXHcIxltX77QRbPnFCWb7Ur1aTaSu76xIV886Fm/qlpC//y7HYqs2L+lFrmThlH25EcddUVTK6tZGpdFTMn1DC5thLp+PXRzMauAYNCUha4FXgf0AKskdQYEc8VVLsa2BsRp0haCdwMXCFpCbASOBOYA/xK0qlADvh8RDwuaQKwVtIDBW3eEhFfKddBlktEsH7HQd53xszj+ry9I4aG+jo+d8mpPL/tANv2H2F3WwcbW9tp78zR3pGjqzuO7jOhpoKG+jrOmD2RPzlnNhNqKo9rn81s7ChlRHEB0BwRGwEk3QOsAAqDYgVwQ7J8H/BN5f+cXQHcExEdwMuSmoELIuL3wDaAiDgo6Xlgbp82R53Wtg72tHcelwvZ/anMZjh73mTOnvf68oigvbObPW0dbN1/hFd2t7NxVzvPvLqfHz3ewpLZE7no5Hoa6muLjjR8esrM+lNKUMwFthSstwAX9lcnInKS9gP1SfmjffadW7ijpAbgPGB1QfG1kj4GNJEfeYyKL5I+3heyB0MS46srGF9dwYL6Oi46uZ6eCLbsOcTTLft5Ystennl1P7Mn1fCe02awZM5EMj41ZWYlKOWup2LvJlFindR9JY0HfgR8LiIOJMXfAhYB55IfdXy1aKekayQ1SWpqbW0tVqXsXth2/G6NLYeMxEn1dfzJOXO4bvkZfPi8uXR193D3Y5u59eFmntu6n4i+L6WZ2euVMqJoAeYXrM8DtvZTp0VSBTAJ2JO2r6RK8iHxjxHx494KEXH0HlBJ3wZ+VqxTEXEbcBvA0qVLj8u73RNb9jJ38jjqx1cfj6crq6qKDG9rmMpbF0zh6ZZ9PPTCTr6/ejNzJtVw8RkziQhf/DazokoZUawBFktaKKmK/MXpxj51GoGrkuXLgIci/6dqI7BSUrWkhcBi4LHk+sXtwPMR8beFDUmaXbD6YeDZwR7UcIgI1r6yl/NPmjLSXTkm2Yw4b8EUPnfJqVz21nkcyfVw16Ov8OG/+x2/a9410t0zs1FowBFFcs3hWuB+8rfH3hER6yTdCDRFRCP5N/27kovVe8iHCUm9H5K/SJ0DPh0R3ZLeBXwUeEbSk8lT9d4G+zeSziV/imoT8MkyHu+Qbd1/hB0HOk74oOiVzYi3njSFc+ZP5vFX9vLoy7v5s++s5p2n1POFZadx3oKxcZxmduxK+hxF8ga+qk/ZlwqWjwCX97PvTcBNfcp+Q/HrF0TER0vp0/G29pX89fSxEhS9shnxtoVT+es/fQv/uHozf/dwMx/+u99xyRkz+fyyUzlj9vBOfmhmo5+n8CjR46/sZVxldlTe8VQONZVZrn7XQh75z+/hC8tOZfXLu/nAN/6Nz/zgCZp3to1098xsBHkKjxKtfWUv586fTEV2bGdrXXUF1753MX9+0Un8/SMb+d5vN/Gzp7dyzrzJvOf0GUzr50K+P4dhNnY5KEpwqDPHc9sO8Bd/sGhY2h9orqaRMLm2iv+y/HSuftdCbntkI9/97cs81bKPc+dP4b2nz2BqXdVId9HMjhMHRQmebtlPd0+MuesThdLCqqG+ji8sO41HXmxl9ct7eKplH+9cVM8fnjZj2L7lz8xGDwdFCXovZJ+3YPII92TkTKip5I/OnsO7F0/nl89t55GXdrF28z6WLZk5pgPUzBwUJXn8lb0sml7H5Fqfbpk4rpLLzp/PRSfX8/Ont/GTJ17l0Y27OX3WBJY2TB3p7pnZMBjbV2bLoCPXzWOb9vA2vwm+zrwptVzz705m5dvmc6izm8v//vd86afP0taRG+mumVmZeUQxgN817+bgkRzLzjy+U4ufCCRx9rzJnDZrAq/sPsSdv9/EA8/t4KYPn8V7T/fvy2ys8IhiAKue2caE6greecq0ke7KqFVdkeWGD57JfZ96B+OrK/iP32vis/c8we62jpHumpmVgYMiRVd3Dw88v4NLlsykusJ39wzk/JOm8LPPvIvPXbKYVc9s45K//TU/eaLFM9SaneAcFCke3bibfYe6uPSsWSPdlVHv7tWbuXv1Zn609lVmTKjh//jDUxhfXcFf3vsU7//aI7TsPTTSXTSzIXJQpFj1zHbqqrL8u1Onj3RXTjgzJ9bwyT9YxB+fPZtNuw6x7JZH+O5vXybX3TPSXTOzQXJQ9CPX3cMv123nvWfM9IfKhigj8Y5F0/jsJYt5W8NU/ts/P8eyrz3CL57d7tNRZicQB0U/Hnmpld3tnT7tVAZTaqv43n94G9/+2FIyEp/6/lo+8I3f8JMnWujyCMNs1HNQFNHTE3z1ly8yf+o4LjnDt3mWgyTet2Qmv/jsu/mby86mq7uHv7z3Kd7+1w9xQ+M6Ht+8l54ejzLMRiN/jqKInz+zjXVbD3DLFedQVeEsLYe+c0l9/B0NvLTjIE2v7OWuR1/he7/bxOTaSt5+cj3nnzSFM2ZP5LRZE6ivq/JXtJqNMAdFH13dPXz1l+s5fdYEPnjO3JHuzpiVkTht1kROmzWRw53dvLD9AAH8fsNu/uXZ7UfrjavMMm/KOOZOGcehzm6mjKtkUm0Vk8dVMqm2kok1lWQz+SDxVOdmw8NB0ccPHtvMpt2HuP2qpUffgGx4javKct6CKUff6He1dfD8tgO8tKONlr2HeXXfIV7dd5gNO9s53NX9un0FTKipYHJtFb/fuJuG+lpOqq9j4bRaGurrmOoRidkxc1AUeHj9Tv77z57jHYvqee/pM0a6O286fU9P1VRmOWXGeE6ZMf5oWUeum/2Huth/uIt9h7vYd3S5k6e27OPnT2+l8FLHhJoKFk6ro6G+jpOn17Fo+ngWTR/Pwml1jKvy3WxmpSgpKCQtB74OZIHvRMSX+2yvBv4BOB/YDVwREZuSbdcDVwPdwGci4v60NiUtBO4BpgKPAx+NiM5jO8yB/X7Dbj5111pOnTmBb/35+f4rdJSqrsgyY2KWGRNrim7P9fSwr72LXe0d7G7rZFdbB7vbO/m3l1r556e20pshEsyZNI5FM8azqCBATqqvZfqEairH+DcZmg3GgEEhKQvcCrwPaAHWSGqMiOcKql0N7I2IUyStBG4GrpC0BFgJnAnMAX4l6dRkn/7avBm4JSLukfS/k7a/VY6DLWbfoU6+9esNfO+3m1gwtZa7rr6QSeMqh+vpbJhVZDJMm1DNtAlv/MrWru4eLlg4lQ2tbWzY2c7GXW1saG1jzct73nBKq76uihkTa5g5sZoptVVMqKlgfHUFE2oqGV9TwcTC9eoKJtRUUF2ZoaYyS3VFhqpspt8/NiKCru7g4JEuDhzJceBwflR04EgXBw7neOTFVg53dXO4q5sjyaOjq4dMRlRkxEn1tVRXZKmuzDAh6cOEmtf6NiHpX2Hf6qoqyPhUqg1RKSOKC4DmiNgIIOkeYAVQGBQrgBuS5fuAbyr/v2QFcE9EdAAvS2pO2qNYm5KeB94L/FlS586k3WEJih+tbeGGxnW0deZYcc4c/usfneGv+BzDKrMZnti8D4DpE6qZPqGaCxfW0xPBgcNd7GrrZE9759E38INHunhpRxuHOnMc6eqhI9fNYO7gra7IB0dVRYaenqAz10Nnd/4x0OcNM8pfyK+pzDKuKktVNkNPBIe7eli39QC5nqCru4eOrh6OdHUzULcEVFVkqMhmqMyIqeOr8mFTkaGmMkN1RfYNP3v7f/RnZYaaJKCqKzJUVxapU5HfN5sRWQllICuRkZA4et0vAiLpde/v4uhPomA5H6y9xxeRFBbUe23ba78FSYj8yDF//ALl10X+ZoqMRCaTX84m/TteZxL69nWwOnLdtB7sYOfBDuZNHtfvCLtcSgmKucCWgvUW4ML+6kRETtJ+oD4pf7TPvr23EhVrsx7YFxG5IvXLbsbEai5aVM+Gc9FvAAAHvElEQVTnl53K6bMmDtfT2CiXkZhcWzXgF1P1jgSO5LqPvkF35Hp/dtPVHeR6glx3T7LcQy75mZHIJiOCbCZDRVb5EEhGIb2h0LtcmVXJbyARQWd3D0f69Kl3JHIk1320vLc/syePoyMJv46uHvYd6jwahh25HvYf7iLXnQ+jN9OnWzJKQiQjMnp9yAVAvD68ct3xxsA7Wi9fIOlo/WJ/IKj3OZOgOtqH5HkzBWWdyR8HnQUfVP3vHzqLj1500vD9UigtKIr9a+17uP3V6a+82AngtPpv7JR0DXBNstomaX2xeqX4zlB3LM00YNfwPsWo42Me+95sxwuj9Jg/djN8bOi7l5QwpQRFCzC/YH0esLWfOi2SKoBJwJ4B9i1WvguYLKkiGVUUey4AIuI24LYS+j+iJDVFxNKR7sfx5GMe+95sxwtvzmPuVcqtHWuAxZIWSqoif3G6sU+dRuCqZPky4KHIn4RrBFZKqk7uZloMPNZfm8k+DydtkLT506EfnpmZHasBRxTJNYdrgfvJ38p6R0Ssk3Qj0BQRjcDtwF3Jxeo95N/4Ser9kPyF7xzw6YjoBijWZvKU/wW4R9L/AJ5I2jYzsxEiT/c8vCRdk5wme9PwMY99b7bjhTfnMfdyUJiZWSp//NTMzFI5KIaRpOWS1ktqlnTdSPenHCTNl/SwpOclrZP02aR8qqQHJL2U/JySlEvSN5LfwdOS3jqyRzB0krKSnpD0s2R9oaTVyTHfm9yYQXLzxr3JMa+W1DCS/R4KSZMl3SfpheS1fvtYf40l/WXyb/pZST+QVDOWX+PBcFAMk4KpTy4FlgBXJlOanOhywOcj4gzgIuDTyXFdBzwYEYuBB5N1yB//4uRxDcM4Hctx8Fng+YL13ulmFgN7yU83AwVT2gC3JPVONF8HfhERpwPnkD/uMfsaS5oLfAZYGhFnkb/Jpnc6orH6GpcuIvwYhgfwduD+gvXrgetHul/DcJw/JT9n13pgdlI2G1ifLP89cGVB/aP1TqQH+c/0PEh+ipmfkf9w6C6gou/rTf5uvrcnyxVJPY30MQziWCcCL/ft81h+jXltdompyWv2M+D9Y/U1HuzDI4rhU2zqkzH1TUjJcPs8YDUwMyK2ASQ/e+dpHyu/h68B/xnonTshbbqZ101pA/ROaXOiOBloBb6bnGr7jqQ6xvBrHBGvAl8BNgPbyL9maxm7r/GgOCiGT8nTkZyIJI0HfgR8LiIOpFUtUnZC/R4k/TGwMyLWFhYXqRolbDsRVABvBb4VEecB7bx2mqmYE/14Sa63rAAWkp/puo78KbW+xsprPCgOiuFTytQnJyRJleRD4h8j4sdJ8Q5Js5Pts4GdSflY+D28E/igpE3kvyvlveRHGJOTKWvg9cd19Jj7TGlzomgBWiJidbJ+H/ngGMuv8SXAyxHRGhFdwI+BdzB2X+NBcVAMn1KmPjnhKD+l6e3A8xHxtwWbCqdxKZx6pRH4WHJnzEXA/t7TFyeKiLg+IuZFRAP51/GhiPgI/U8309+UNieEiNgObJF0WlJ0MfnZFcbsa0z+lNNFkmqTf+O9xzwmX+NBG+mLJGP5AXwAeBHYAPzVSPenTMf0LvJD7KeBJ5PHB8ifn30QeCn5OTWpL/J3f20AniF/V8mIH8cxHP8fAj9Llk8mP3dZM/BPQHVSXpOsNyfbTx7pfg/hOM8FmpLX+f8Dpoz11xj4b8ALwLPAXUD1WH6NB/PwJ7PNzCyVTz2ZmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYHQeSPiXpY2Vu83uSLkuWvzNGZie2UWjA78w2ezORVBGvTQJXNhHxv8vdZp/2PzGc7dubm0cUNiZJqpP0c0lPJV9Ec4Wk8yX9WtJaSfcXzFv0r5L+p6RfA58t/Es92d6W/PzDZP8fSnpR0pclfUTSY5KekbQopT83SPpCwfPdnOz3oqR3J+VnJmVPJl8AtFhSg6RnC9r5gqQbirT/r5KW9vZX0k3JsT8qaWZ5fqv2ZuWgsLFqObA1Is6J/BfR/AL4X8BlEXE+cAdwU0H9yRHxBxHx1QHaPYf8Fxi9BfgocGpEXAB8B/g/B9G/imS/zwH/T1L2KeDrEXEusJT8xHNDUQc8GhHnAI8A/2mI7ZgBPvVkY9czwFck3Uz+S2j2AmcBD+TnfCNL/nsHet1bYrtrIpnwTtIG4JcFz/eeQfSvd9bdtUBDsvx74K8kzQN+HBEvJX0drE7yx9zb/vuG0ohZLweFjUkR8aKk88lPWPjXwAPAuoh4ez+7tBcs50hG28lMolUF2zoKlnsK1nsY3P+n3v26e/eLiLslrQb+CLhf0ifITypZOPKvKaHtrnhtErej7ZsNlU892ZgkaQ5wKCK+T/6byy4Epkt6e7K9UtKZ/ey+CTg/WV4BVA5zd0n6dDKwMSK+QX4a67OBHcAMSfWSqoE/Ph59MSvkvzRsrHoL8P9K6gG6gL8gP1L4hqRJ5P/tfw1YV2TfbwM/lfQY+em024vUGQ5XAH8uqQvYDtwYEV2SbiT/dbMvk58G2+y48jTjZmaWyqeezMwslU89mZWRpL8CLu9T/E8RcVOx+mYnAp96MjOzVD71ZGZmqRwUZmaWykFhZmapHBRmZpbKQWFmZqn+fyJecWmKlHQWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6da8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6f65e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6faa4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6d38780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f70734e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = True)\n",
    "    plt.show()\n",
    "#除开0缺失值，数据基本满足正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9f6faa7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True);\n",
    "#特征之间基本没有较强的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                              0\n",
      "Plasma_glucose_concentration           0\n",
      "blood_pressure                         0\n",
      "Triceps_skin_fold_thickness            0\n",
      "serum_insulin                          0\n",
      "BMI                                    0\n",
      "Diabetes_pedigree_function             0\n",
      "Age                                    0\n",
      "Target                                 0\n",
      "Triceps_skin_fold_thickness_Missing    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#缺失值比较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值，但由于缺失值在案例中观察是比较随机产生，故还是使用中值填补。\n",
    "medians = train.median() \n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化\n",
    "y_train=train['Target']\n",
    "x_train=train.drop(['Target'],axis=1)\n",
    "feat_names=x_train.columns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_x=StandardScaler()\n",
    "x_train=ss_x.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>-0.647760</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>-0.647760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1.543781</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>-0.647760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>-0.647760</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Triceps_skin_fold_thickness_Missing  \\\n",
       "0                    0.468492  1.425995                            -0.647760   \n",
       "1                   -0.365061 -0.190672                            -0.647760   \n",
       "2                    0.604397 -0.105584                             1.543781   \n",
       "3                   -0.920763 -1.041549                            -0.647760   \n",
       "4                    5.484909 -0.020496                            -0.647760   \n",
       "\n",
       "   Target  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       0  \n",
       "4       1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#存为csv格式\n",
    "x_train=pd.DataFrame(columns=feat_names,data=x_train)\n",
    "train=pd.concat([x_train,y_train],axis=1)\n",
    "train.to_csv('FE-pima-indians-diabetes.csv',index=False,header=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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